# Blockchain-based Federated Learning with SMPC Model Verification Against Poisoning Attack for Healthcare Systems

arXiv: https://arxiv.org/abs/2304.13360 · HTML: https://mlsanity.com/paper/2304.13360/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2023.

## Built on by (most cited first, 10 of 10)

- [The Rise of Small Language Models in Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2504.17119/) (2025)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [Green Federated Learning: A new era of Green Aware AI](https://mlsanity.com/paper/2409.12626/) (2024)
- [Defending Against Poisoning Attacks in Federated Learning with Blockchain](https://mlsanity.com/paper/2307.00543/) (2023)
- [Privacy-Preserving in Blockchain-based Federated Learning Systems](https://mlsanity.com/paper/2401.03552/) (2024)
- [FedRFQ: Prototype-Based Federated Learning with Reduced Redundancy, Minimal Failure, and Enhanced Quality](https://mlsanity.com/paper/2401.07558/) (2024)
- [Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions](https://mlsanity.com/paper/2403.00873/) (2024)
- [SoK: Security and Privacy Risks of Healthcare AI](https://mlsanity.com/paper/2409.07415/) (2024)
- [Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems](https://mlsanity.com/paper/2602.08290/) (2026)
- [PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT](https://mlsanity.com/paper/2602.17973/) (2026)

## Abstract (arXiv)

Due to the rising awareness of privacy and security in machine learning applications, federated learning (FL) has received widespread attention and applied to several areas, e.g., intelligence healthcare systems, IoT-based industries, and smart cities. FL enables clients to train a global model collaboratively without accessing their local training data. However, the current FL schemes are vulnerable to adversarial attacks. Its architecture makes detecting and defending against malicious model updates difficult. In addition, most recent studies to detect FL from malicious updates while maintaining the model's privacy have not been sufficiently explored. This paper proposed blockchain-based federated learning with SMPC model verification against poisoning attacks for healthcare systems. First, we check the machine learning model from the FL participants through an encrypted inference process and remove the compromised model. Once the participants' local models have been verified, the models are sent to the blockchain node to be securely aggregated. We conducted several experiments with different medical datasets to evaluate our proposed framework.